Purpose: To explore the feasibility of the linear force–velocity (F–V) modeling approach to detect selective changes of F–V parameters (ie, maximum force [F 0], maximum velocity [V 0], F–V slope [a], and maximum power [P 0]) after a sprint-training program. Methods: Twenty-seven men were randomly assigned to a heavy-load group (HLG), light-load group (LLG), or control group (CG). The training sessions (6 wk × 2 sessions/wk) comprised performing 8 maximal-effort sprints against either heavy (HLG) or light (LLG) resistances in leg cycle-ergometer exercise. Pre- and posttest consisted of the same task performed against 4 different resistances that enabled the determination of the F–V parameters through the application of the multiple-point method (4 resistances used for the F–V modeling) and the recently proposed 2-point method (only the 2 most distinctive resistances used). Results: Both the multiple-point and the 2-point methods revealed high reliability (all coefficients of variation <5% and intraclass correlation coefficients >.80) while also being able to detect the group-specific training-related changes. Large increments of F 0, a, and P 0 were observed in HLG compared with LLG and CG (effect size [ES] = 1.29–2.02). Moderate increments of V 0 were observed in LLG compared with HLG and CG (ES = 0.87–1.15). Conclusions: Short-term sprint training on a leg cycle ergometer induces specific changes in F–V parameters that can be accurately monitored by applying just 2 distinctive resistances during routine testing.
Amador García-Ramos, Alejandro Torrejón, Alejandro Pérez-Castilla, Antonio J. Morales-Artacho and Slobodan Jaric
Amador García-Ramos, Alejandro Torrejón, Antonio J. Morales-Artacho, Alejandro Pérez-Castilla and Slobodan Jaric
This study determined the optimal resistive forces for testing muscle capacities through the standard cycle ergometer test (1 resistive force applied) and a recently developed 2-point method (2 resistive forces used for force-velocity modelling). Twenty-six men were tested twice on maximal sprints performed on a leg cycle ergometer against 5 flywheel resistive forces (R1–R5). The reliability of the cadence and maximum power measured against the 5 individual resistive forces, as well as the reliability of the force-velocity relationship parameters obtained from the selected 2-point methods (R1–R2, R1–R3, R1–R4, and R1–R5), were compared. The reliability of outcomes obtained from individual resistive forces was high except for R5. As a consequence, the combination of R1 (≈175 rpm) and R4 (≈110 rpm) provided the most reliable 2-point method (CV: 1.46%–4.04%; ICC: 0.89–0.96). Although the reliability of power capacity was similar for the R1–R4 2-point method (CV: 3.18%; ICC: 0.96) and the standard test (CV: 3.31%; ICC: 0.95), the 2-point method should be recommended because it also reveals maximum force and velocity capacities. Finally, we conclude that the 2-point method in cycling should be based on 2 distant resistive forces, but avoiding cadences below 110 rpm.
Amador García-Ramos, Alejandro Torrejón, Belén Feriche, Antonio J. Morales-Artacho, Alejandro Pérez-Castilla, Paulino Padial and Guy Gregory Haff
Purpose: To provide 2 general equations to estimate the maximum possible number of repetitions (XRM) from the mean velocity (MV) of the barbell and the MV associated with a given number of repetitions in reserve, as well as to determine the between-sessions reliability of the MV associated with each XRM. Methods: After determination of the bench-press 1-repetition maximum (1RM; 1.15 ± 0.21 kg/kg body mass), 21 men (age 23.0 ± 2.7 y, body mass 72.7 ± 8.3 kg, body height 1.77 ± 0.07 m) completed 4 sets of as many repetitions as possible against relative loads of 60%1RM, 70%1RM, 80%1RM, and 90%1RM over 2 separate sessions. The different loads were tested in a randomized order with 10 min of rest between them. All repetitions were performed at the maximum intended velocity. Results: Both the general equation to predict the XRM from the fastest MV of the set (CV = 15.8–18.5%) and the general equation to predict MV associated with a given number of repetitions in reserve (CV = 14.6–28.8%) failed to provide data with acceptable between-subjects variability. However, a strong relationship (median r2 = .984) and acceptable reliability (CV < 10% and ICC > .85) were observed between the fastest MV of the set and the XRM when considering individual data. Conclusions: These results indicate that generalized group equations are not acceptable methods for estimating the XRM–MV relationship or the number of repetitions in reserve. When attempting to estimate the XRM–MV relationship, one must use individualized relationships to objectively estimate the exact number of repetitions that can be performed in a training set.